THE OPPORTUNITY Silvus is seeking a Machine Learning Engineer who will report to the R&D Director, Machine Learning on the R&D team. The successful individual in this role will focus on applying ...
THE OPPORTUNITY Silvus is seeking a Machine Learning Engineer who will report to the R&D Director, Machine Learning on the R&D team. The successful individual in this role will focus on applying ...
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Quick apply
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Machine Learning Petroleum Engineer information
How does a machine learning petroleum engineer typically collaborate with geoscientists and drilling teams to optimize oil and gas production?
What is the difference between Machine Learning Petroleum Engineer vs Reservoir Engineer?
| Aspect | Machine Learning Petroleum Engineer | Reservoir Engineer |
|---|---|---|
| Required Credentials | Bachelor's/Master's in Petroleum Engineering, Data Science, or related fields; knowledge of machine learning | Bachelor's/Master's in Petroleum Engineering or Geosciences; strong understanding of reservoir simulation |
| Work Environment | Data analysis, modeling, software development in oil & gas companies | Reservoir modeling, field development planning in oil & gas operations |
| Industry Usage | Applying machine learning to optimize extraction, predict reservoir behavior | Estimating reservoir properties, managing production strategies |
The Machine Learning Petroleum Engineer focuses on integrating data science and machine learning techniques to optimize oil extraction processes, while the Reservoir Engineer specializes in modeling and managing subsurface reservoirs to maximize recovery. Both roles are vital in the oil & gas industry but differ in their core skills and daily tasks.
What is a machine learning petroleum engineer?
What are the key skills and qualifications needed to thrive as a machine learning petroleum engineer?
Job description
THE OPPORTUNITY
Silvus is seeking a Machine Learning Engineer who will report to the R&D Director, Machine Learning on the R&D team. The successful individual in this role will focus on applying machine learning and data-driven techniques to improve the performance, efficiency, and adaptability of Silvus' advanced MIMO radios and wireless networking systems. This individual will work closely with experts in wireless communications, DSP, networking, and embedded systems to develop ML-driven features that solve real-world problems in dynamic and challenging RF environments.
This position is based at Silvus Technologies' headquarters in the heart of vibrant West Los Angeles, CA, and is on a hybrid schedule. A minimum of 3 days onsite per week is expected. On-site days are Mondays, Wednesdays, and Thursdays.
The following is a list of at least some of the current essential job functions of the position. Management may assign or reassign duties and responsibilities at any time at its discretion.
ROLE AND RESPONSIBILITIES
- Research, design, and implement machine learning algorithms to enhance performance in wireless communication systems (e.g., link adaptation, interference mitigation, anomaly detection, spectrum sensing).
- Analyze real-world RF datasets to extract insights and develop predictive models.
- Develop software prototypes and integrate ML algorithms with Silvus' radio firmware and networking stack.
- Collaborate with cross-functional teams to define ML use cases and evaluate the impact of deployed models.
- Contribute to the design of data pipelines and infrastructure for training, testing, and validating models.
- Participate in performance benchmarking and iterative improvement cycles.
- Stay current with the latest Machine Learning research for wireless and embedded systems.
- Perform other related duties of which the above are representative.
REQUIRED QUALIFICATIONS
- Bachelor of Science degree in Electrical Engineering, Computer Science, Computer Engineering, or related field plus a minimum of 2 years of experience in machine learning, with demonstrated application to real-world problems; no experience required with an advance degree (MS or PhD)
- Strong foundation in supervised and unsupervised learning and statistical modeling.
- Experience with Python ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn, etc.).
- Exposure to MATLAB or C/C++ for signal processing algorithm development.
- Must be a U.S. Citizen due to clients under U.S. government contracts.
- All employment is contingent upon the successful clearance of a background check and drug test.
PREFERRED KNOWLEDGE, SKILLS, AND ABILITIES
- MS. or Ph.D. in Electrical Engineering, Computer Science, or a related field.
- Demonstrated experience with RF signal classification, anomaly detection, or spectrum monitoring.
- Proficiency in MATLAB or C/C++ for signal processing algorithm development.
- Familiarity with wireless communication concepts (e.g., PHY/MAC layers, MIMO, OFDM, spectrum access).
- Familiarity with embedded ML, real-time systems, or deploying ML on edge devices.
- Background in adaptive modulation, beamforming, or cognitive radio techniques.
- Experience working with wireless standards such as 3GPP, IEEE 802.11/15, or military waveforms.
- Experience with GPU acceleration or model optimization for constrained environments.
- Excellent communication and collaboration skills.
WORKING CONDITIONS AND PHYSICAL REQUIREMENTS
- Office environment.
- Outdoor environment for demos.
- Occasional exposure to heat, cold, and allergens while performing tests or demonstrations in the field.
- While performing the duties of this job, the employee is required to do the following:
- Lift equipment up to 20 lbs. for the set-up of demonstrations and testing.
- Perform bending and reaching movements to place items on lower and higher shelves.
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About SILVUS TECHNOLOGIES
Sourced by ZipRecruiter
Industry
Wireless telecommunications carriers
Company size
51 - 200 Employees
Headquarters location
Los Angeles, CA, US
Year founded
2004